In this branch we will cover the starting steps of creating a GPU accelerated Docker Image for DSMLP. It's recommended to follow the steps in the "master" branch before continuing.
Note: our standard scipy-ml-notebook has CUDA installed with a compatible PyTorch and Tensorflow for use on DSMLP. Please use that image if that's all you require since installing a working CUDA environment is time consuming.
As of Fall 2020, there are 15 GPU nodes on the DSMLP cluster available for classroom use, and each node has 8 NVIDIA GPUs installed. These GPUs are dynamically assigned to a container on start-up when requested and will stay attached until that container is deleted, meaning a GPU will remain occupied even if it's actually not running anything.
| GPU Model | VRAM | Amount | Node |
|---|---|---|---|
| NVIDIA 1080 Ti | 11GB | 80 | n01 through n12 except n09, n10 |
| NVIDIA 2080 Ti | 11GB | 32 | n18, n21, n22, n24 |
| NVIDIA 1070 Ti | 8GB | 7 | n10 |
Note: The Datahub/DSMLP cannot guarantee that versions of Python libraries that use particular CUDA versions will work on the platform as CUDA versions must be synced to specific Linux x86_64 Driver Versions
The graphics driver will be installed automatically on the container on start-up. It can be challenging to get CUDA compatibility working in a custom environment. You can find the latest CUDA Toolkit supported on DSMLP by checking how we install it within our scipy-ml-notebook container. The version of CUDA in this Dockerfile is the latest version of CUDA the DSMLP supports, and versions > what's defined may not work on the platform.
Please see the scipy-ml-notebook Dockerfile for supported TensorFlow and Pytorch versions.